1. Field of the Invention
The present invention is directed to a system which builds a database of images, which retrieves images from the database based on a "query" image, and which determines whether the retrieved images are similar to the query image. In building the database, the system generates binary representations for regions of an image, and then uses these binary representations to store pointers to the image in binary trees. In retrieving images from the database, the system generates binary representations of the query image, selects an image from the database by traversing its binary trees using the binary representations, and retrieves pointers for the image from the binary trees.
2. Description of the Related Art
In recent years, it has become commonplace for personal computers and other digital hardware to process and display digital images. This is due, in no small part, to the increase in popularity of digital video on the Internet. As a result of this increased use of digital imagery, it has become necessary to retrieve images from increasingly larger image databases. This typically does not present a problem if there is some way to readily identify the images, such as via a file name or the like.
On the other hand, it is more difficult to retrieve images from a large database if the only way to retrieve the images is based on their content, as is oftentimes tile case, e.g., for digital video and for scanned images. For example, a user may wish to locate a video clip based on a single frame of video, or to locate a particular frame of video within a video clip. Similarly, a user may wish to determine which of a plurality of pre-stored images best matches a scanned image. In these cases, image retrieval can be difficult.
In view of the foregoing, researchers have developed systems for selecting and retrieving an image based on its content. In general, these systems operate by inputting a "query" image, such as an individual frame of digital video, a scanned image, or the like, determining which image in a database is similar to the query image, and then retrieving that image. Conventional systems of this type, however, suffer from several drawbacks, particularly with respect to operational speed.
Specifically, conventional content-based image retrieving systems tend to be relatively slow. That is, conventional systems typically rely on some form of feature extraction and comparison in order to determine which images in the database are similar to the query image. While such comparisons can provide accurate results, they require a significant amount of processing, particularly in cases where the images have a relatively high resolution, or where there are numerous comparisons to be made. This excess processing significantly slows down the process.
Accordingly, there exists a need for a content-based image retrieval system that addresses the foregoing and other drawbacks of conventional systems. In particular, there exists a need for a system which provides for accurate content-based image retrieval, and which also operates more quickly than its conventional counterparts.